an efficient framework for glioma tumor classifications and diagnosis using proposed cnn architecture

This article proposes the deep learning algorithm- convolutional neural networks (cnn) for both glioma tumor classifications and diagnosis process. this proposed cnn architecture is derived from the conventional cnn architecture to obtain the optimum classification and diagnosis accuracy. this proposed cnn architecture is derived from the conventional system for obtaining the high classification and diagnosis performance. this proposed methodology stated in this paper uses brats 2015 open access dataset for obtaining the brain magnetic resonance image (mri) for tumor region detection. the proposed methodology stated in this paper for tumor diagnosis achieves 97.7% of jaccard index (j) and 83.8% of dice similarity index (dsi) and 99.025 of diagnosis rate (dr) using cnn algorithm.